Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Browse subjects
  3. Computer Science
  4. Pattern Recognition and Machine Learning
  5. Content listing

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

2275 results in Pattern Recognition and Machine Learning

Page 27 of 114

  • 20 - Multiple Testing

  • from Part III - Elements of Statistical Inference
  • Ery Arias-Castro, University of California, San Diego
  • Book:
    Principles of Statistical Analysis
    Published online:
    22 July 2022
    Print publication:
    25 August 2022, pp 309-328

  • Summary

    In a wide range of real-life situations, not one but several, even many hypotheses are to be tested, and not accounting for multiple inference can lead to a grossly incorrect analysis. In this chapter we look closely at this important issue, describing some pitfalls and presenting remedies that `correct’ for this multiplicity. Combination tests assess whether there is evidence against any of the null hypotheses being tested. Other procedures aim instead at identifying the null hypotheses that are not congruent with the data while controlling some notion of error rate.

  • 22 - Foundational Issues

  • from Part III - Elements of Statistical Inference
  • Ery Arias-Castro, University of California, San Diego
  • Book:
    Principles of Statistical Analysis
    Published online:
    22 July 2022
    Print publication:
    25 August 2022, pp 356-370

  • Summary

    Randomization was presented in a previous chapter as an essential ingredient in the collection of data, both in survey sampling and in experimental design. We argue here that randomization is the essential foundation of statistical inference: It leads to conditional inference in an almost canonical way, and allows for causal inference, which are the two topics covered in the chapter.

  • 14 - One Proportion

  • from Part III - Elements of Statistical Inference
  • Ery Arias-Castro, University of California, San Diego
  • Book:
    Principles of Statistical Analysis
    Published online:
    22 July 2022
    Print publication:
    25 August 2022, pp 192-202

  • Summary

    Estimating a proportion is one of the most basic problems in statistics. Although basic, it arises in a number of important real-life situations. Examples include election polls, conducted to estimate the proportion of people that will vote for a particular candidate; quality control, where the proportion of defective items manufactured at a particular plant or assembly line needs to be monitored, and one may resort to statistical inference to avoid having to check every single item; and clinical trials, which are conducted in part to estimate the proportion of people that would benefit (or suffer serious side effects) from receiving a particular treatment. The fundamental model is that of Bernoulli trials. The binomial family of distributions plays a central role. Also discussed are sequential designs, which lead to negative binomial distributions.

  • 16 - One Numerical Sample

  • from Part III - Elements of Statistical Inference
  • Ery Arias-Castro, University of California, San Diego
  • Book:
    Principles of Statistical Analysis
    Published online:
    22 July 2022
    Print publication:
    25 August 2022, pp 230-270

  • Summary

    We consider an experiment that yields, as data, a sample of independent and identically distributed (real-valued) random variables with a common distribution on the real line. The estimation of the underlying mean and median is discussed at length, and bootstrap confidence intervals are constructed. Tests comparing the underlying distribution to a given distribution (e.g., the standard normal distribution) or a family of distribution (e.g., the normal family of distributions) are introduced. Censoring, which is very common in some clinical trials, is briefly discuss.

Page 27 of 114